Substitution-Mutation Rate Ratio (c/µ) As Molecular Adaptation Test Beyond Ka/Ks: A SARS-COV-2 Case Study

Area of Science:

• Genomics
• Molecular Evolution
• Bioinformatics

Background:

• The Ka/Ks ratio is a standard metric for assessing evolutionary fitness changes due to mutations in genomic translated regions.
• However, its accuracy is contingent on the assumption of neutral synonymous mutations, limiting its applicability to non-coding regions and potentially misinterpreting fitness changes.
• This necessitates a more robust method for detecting selection pressure across diverse genomic contexts.

Purpose of the Study:

• To address the limitations of the Ka/Ks ratio test in accurately quantifying fitness changes.
• To introduce and validate a generalized substitution-mutation rate ratio (c/µ) test for detecting selection pressure.
• To compare the performance of the c/µ test against the traditional Ka/Ks test using SARS-CoV-2 genomic data.

Main Methods:

• Derived a general equation linking the c/µ ratio with weighted Ks/µ and Ka/µ values, accounting for synonymous and nonsynonymous site proportions.
• Applied both the c/µ and Ka/Ks tests to analyze 25 proteins from SARS-CoV-2.
• Utilized three independent genomic sequence datasets for comparative analysis.

Main Results:

• The study derived a generalized equation for the c/µ test, establishing its relationship with Ka/Ks under different neutrality assumptions.
• Comparative analysis revealed that the Ka/Ks test inaccurately reported fitness changes for 7 out of 25 proteins in SARS-CoV-2.
• The c/µ test demonstrated broader applicability and accuracy in detecting selection pressure compared to Ka/Ks.

Conclusions:

• The Ka/Ks ratio test's reliance on neutral synonymous mutations can lead to inaccurate assessments of fitness changes.
• The substitution-mutation rate ratio (c/µ) test provides a more generalized and accurate method for detecting selection pressure across genomic regions.
• The c/µ test is recommended to complement the Ka/Ks test for comprehensive genomic selection pressure analysis.